
# Importing the necessary libraries

import pandas as pd
import numpy as np


#Reading my file

# I got a tokenizing error. I opened my file in excel to see what could be the problem.
#I discovered it had a # seperator 

df = pd.read_csv("data_abschlussprojekt.csv", sep="#")
df


# Finding out my column names

df.columns

Index(['Unnamed: 0', ' funded_amount', ' loan_amount', ' activity', ' sector',
       ' use', ' country_code', ' country', ' region', ' currency',
       ' term_in_months', ' lender_count', ' borrower_genders',
       ' repayment_interval'],
      dtype='object')


# I discover that my column names have trailing spaces at the end. 
#I adjust for easy use 

df.columns = df.columns.str.replace(' ', '')
df.columns

Index(['Unnamed:0', 'funded_amount', 'loan_amount', 'activity', 'sector',
       'use', 'country_code', 'country', 'region', 'currency',
       'term_in_months', 'lender_count', 'borrower_genders',
       'repayment_interval'],
      dtype='object')


# Let's drop the Unamed column
df = df.drop('Unnamed:0',axis=1)


#Check if there are any duplicate rows

# I will find duplictaes through out the whole table


df.duplicated().sum()

24372


df.loc[df.duplicated()==True,:]


#Deleting my duplicated rows and proofing

df.drop_duplicates(inplace=True)
df.duplicated().sum()

0


df.dtypes

funded_amount         float64
loan_amount           float64
activity               object
sector                 object
use                    object
country_code           object
country                object
region                 object
currency               object
term_in_months        float64
lender_count            int64
borrower_genders       object
repayment_interval     object
dtype: object


#Checking for columns with null values
df.isnull().sum()

funded_amount             0
loan_amount               0
activity                  0
sector                    0
use                    3899
country_code              8
country                   0
region                56163
currency                  0
term_in_months            0
lender_count              0
borrower_genders       3888
repayment_interval        0
dtype: int64


df.isnull().sum() * 100 / len(df)

funded_amount         0.000000
loan_amount           0.000000
activity              0.000000
sector                0.000000
use                   0.602783
country_code          0.001237
country               0.000000
region                8.682767
currency              0.000000
term_in_months        0.000000
lender_count          0.000000
borrower_genders      0.601083
repayment_interval    0.000000
dtype: float64


#Overview of use column with null values

df.loc[(df.use.isnull()),:]


df.loc[(df.borrower_genders.isnull()),:]


# Does a null value in Gender column imply a null in Region and Use column?
df.loc[(df.borrower_genders.isnull()) & (df.region.notnull()),:]


df.loc[(df.borrower_genders.isnull()) & (df.use.notnull()),:]


# In how many countries do we have a null value for gender
missing_gender = df.loc[(df.borrower_genders.isnull()), "country"].nunique()
missing_gender

69


#How many countries are there in our data set

df.country.nunique()

87


df.loc[(df.region.isnull()),:]


#how many countries have a null value for region
missing_region = df.loc[(df.region.isnull()), "country"].nunique()
missing_region

72


df.loc[(df.country_code.isnull()),:]


#I want to find out if there are other applications from Namibia that have a country code imputed
df.loc[(df.country=="Namibia"),:]

#There are no other rows


df["use"].fillna("not_provided", inplace = True)


df['borrower_genders'] = df['borrower_genders'].fillna(df['borrower_genders'].mode()[0])


df.drop(columns=['region'],axis=7, inplace=True)


df["country_code"].fillna("Nam", inplace = True)

# Realised that this data set takes the alpha 2 code instead of the alpha 3 code (ISO norms), so will replace "Nam" with "NA"

df.loc[(df.country_code=="Nam"),"country_code"] ="NA"


#Proofing

df.loc[(df.country=="Namibia"),:]


#Proofing Null values
df.isnull().sum()

funded_amount         0
loan_amount           0
activity              0
sector                0
use                   0
country_code          0
country               0
currency              0
term_in_months        0
lender_count          0
borrower_genders      0
repayment_interval    0
dtype: int64


#Doing some final checks
df.repayment_interval.unique()

array(['irregular', 'bullet', 'monthly', 'weekly'], dtype=object)


#min and max values for this column
df.term_in_months.max()

158.0


df.term_in_months.min()

1.0


df.term_in_months.unique()

array([ 12.,  11.,  43.,  14.,   4.,  13.,  10.,   8.,   5.,  20.,   7.,
         3.,  17.,  15.,   9.,  39.,  23.,  26.,   6.,  30.,  22.,  25.,
        27.,  16.,  52.,  18.,  36.,  19.,  28.,  62.,  32.,  24.,  21.,
        50.,  38.,   2.,  35.,  72., 137.,   1.,  49.,  33.,  42.,  29.,
        37.,  51., 113.,  79.,  31.,  44.,  74.,  34.,  48., 114.,  40.,
       124., 104.,  63.,  85.,  78.,  70.,  86.,  61.,  60.,  67.,  55.,
        53.,  41.,  68., 143.,  77., 130.,  45., 111., 134., 107., 142.,
       148.,  56., 122., 133., 141., 110.,  81., 106.,  54., 147., 112.,
        59., 145., 121., 109.,  80.,  47.,  97.,  75., 101., 128.,  98.,
        87.,  71.,  66.,  46., 125.,  76.,  73., 120., 144., 118., 131.,
        65., 108.,  58., 123.,  84.,  99.,  82.,  92.,  69.,  91.,  57.,
        90.,  93., 129.,  89.,  88.,  64., 126., 138., 158.,  83., 100.,
       105., 132.,  96., 127., 135.,  95., 154., 156.,  94., 115., 102.,
       116., 136., 103., 139., 146.])


#creating categories for this column
category = ["short_term_loan", "medium_term_loan", "long_term_loan","extra_long_term_loan"]

max_value = df.loc[:,"term_in_months"].max()
min_value = df.loc[:,"term_in_months"].min()

term_range = [min_value - .001 * abs(min_value), 25, 61, 121,max_value + .001 * abs(max_value)]

df["term_cat"] = pd.cut(x=df.loc[:,"term_in_months"],
                                      bins=term_range,          
                                                                 
                                      labels=category            
                                     )

df["term_label"] = pd.cut(x=df.loc[:,"term_in_months"],
                                      bins=term_range,         
                                                                 
                                     )

df.head(2)


df["rate_of_financing(%)"]=round((df['funded_amount']/df['loan_amount'])*100,2)
df.head(2)


conditions = [
    (df['rate_of_financing(%)'] == 0),
    (df['rate_of_financing(%)'] > 0) & (df['rate_of_financing(%)'] <= 30),
    (df['rate_of_financing(%)'] > 30) & (df['rate_of_financing(%)'] <= 49),
    (df['rate_of_financing(%)'] > 49) & (df['rate_of_financing(%)'] <= 80),
    (df['rate_of_financing(%)'] > 80) & (df['rate_of_financing(%)'] < 100),
    (df['rate_of_financing(%)'] == 100 ),
    (df['rate_of_financing(%)'] > 100 )
    
              ]
choices = ["not_funded","very_poor_funding","poor_funding" ,"avg_funding", "good_funding","fully_funded","over_funded"]
df['loan_status'] = np.select(conditions,choices, default=0)


df.head(2)


df.dtypes

funded_amount            float64
loan_amount              float64
activity                  object
sector                    object
use                       object
country_code              object
country                   object
currency                  object
term_in_months           float64
lender_count               int64
borrower_genders          object
repayment_interval        object
term_cat                category
term_label              category
rate_of_financing(%)     float64
loan_status               object
dtype: object


#Changing the columns below to category type since the values in this column are repetitive



df_1=df.copy()
list_category = ['activity','sector','country_code','country','repayment_interval','loan_status','currency']

for column in list_category:
    df_1.loc[:,column] = df.loc[:,column].astype('category')


df.term_in_months.unique()

array([ 12.,  11.,  43.,  14.,   4.,  13.,  10.,   8.,   5.,  20.,   7.,
         3.,  17.,  15.,   9.,  39.,  23.,  26.,   6.,  30.,  22.,  25.,
        27.,  16.,  52.,  18.,  36.,  19.,  28.,  62.,  32.,  24.,  21.,
        50.,  38.,   2.,  35.,  72., 137.,   1.,  49.,  33.,  42.,  29.,
        37.,  51., 113.,  79.,  31.,  44.,  74.,  34.,  48., 114.,  40.,
       124., 104.,  63.,  85.,  78.,  70.,  86.,  61.,  60.,  67.,  55.,
        53.,  41.,  68., 143.,  77., 130.,  45., 111., 134., 107., 142.,
       148.,  56., 122., 133., 141., 110.,  81., 106.,  54., 147., 112.,
        59., 145., 121., 109.,  80.,  47.,  97.,  75., 101., 128.,  98.,
        87.,  71.,  66.,  46., 125.,  76.,  73., 120., 144., 118., 131.,
        65., 108.,  58., 123.,  84.,  99.,  82.,  92.,  69.,  91.,  57.,
        90.,  93., 129.,  89.,  88.,  64., 126., 138., 158.,  83., 100.,
       105., 132.,  96., 127., 135.,  95., 154., 156.,  94., 115., 102.,
       116., 136., 103., 139., 146.])


#the term_in_months column has only whole numbers as values. So i will change it to interger

df_1['term_in_months'] = df_1['term_in_months'].astype(int)


#Proofing
df_1.dtypes

funded_amount            float64
loan_amount              float64
activity                category
sector                  category
use                       object
country_code            category
country                 category
currency                category
term_in_months             int32
lender_count               int64
borrower_genders          object
repayment_interval      category
term_cat                category
term_label              category
rate_of_financing(%)     float64
loan_status             category
dtype: object


df_f  = df.groupby(by='loan_status',as_index=False).agg(count_cat=('loan_status','count')).sort_values(['count_cat'],ascending=False)
df_f


import plotly.express as px


fig = px.pie(df_f, 
             values='count_cat', 
             names='loan_status',
             title= "Fig 1.0 : Project funding KIVA 2016"
            )

fig.show()


df.loc[df["loan_status"]== "not_funded",:]


#wheh a loan is a fake or when the loan was not originally applied for in english does it affect its funding ?
df.loc[df["use"].str.startswith('Pretend') | df["use"].str.startswith('Kiva')|df["use"].str.startswith('Translated')|df["use"].str.startswith('Reviewed') ,:]


df.loc[(df.use == 'not_provided') & (df.funded_amount== 0),:]


df.loc[(df.use == 'not_provided') ,:]


#What sectors are fully_funded
df_7 = df.groupby('sector')['loan_status'].apply(lambda x: (x=='fully_funded').sum()).reset_index(name='count').sort_values(['count'],ascending=False)
df_7.head(5)


#what sectors are not funded
df_7 = df.groupby('sector')['loan_status'].apply(lambda x: (x=='not_funded').sum()).reset_index(name='count').sort_values(['count'],ascending=False)
df_7.head(5)


#what countries have applications that were  not funded

df_7 = df.groupby('country')['loan_status'].apply(lambda x: (x=='not_funded').sum()).reset_index(name='count').sort_values(['count'],ascending=False)
df_7.head(5)


df_c  = df.groupby(by='country',as_index=False).agg(number_of_applications=('loan_status','size')).sort_values(['number_of_applications'],ascending=False)
df_c.head(5)


# PLOT 1 TO BE EVALUATED

fig = px.scatter_geo(df_c, locations="country",
                     size="number_of_applications",
                     locationmode = 'country names',
                     color= 'country',
                     size_max=30,
                     title=" Fig 2.0 : Distribution of KIVA Projects accross the world 2016"
                     
                     
                    )

fig.update_layout(plot_bgcolor="white")
            
fig.show()


#Philipines
df_4 = df.groupby('sector')['country'].apply(lambda x: (x=='Philippines').sum()).reset_index(name='count').sort_values(['count'],ascending=False)
df_4.head()


#Kenya
df_5 = df.groupby('sector')['country'].apply(lambda x: (x=='Kenya').sum()).reset_index(name='count').sort_values(['count'],ascending=False)
df_5.head()


#El Salvador
df_6 = df.groupby('sector')['country'].apply(lambda x: (x=='El Salvador').sum()).reset_index(name='count').sort_values(['count'],ascending=False)
df_6.head()


df_s  = df.groupby(by='sector',as_index=False).agg(applications=('sector','size')).sort_values(['applications'],ascending=False)
df_s


barplot4 = px.bar(data_frame=df_s,
                 x="sector",
                 y="applications",
                 color="sector",
                 title="Fig 3.0: Number of Applications per Sector 2016",
                 template= "plotly_dark",
                 labels={"applications":"Total number of applications"}
                 
                 )

barplot4.show()


#Agriculture
df_7 = df.groupby('country')['sector'].apply(lambda x: (x=='Agriculture').sum()).reset_index(name='count').sort_values(['count'],ascending=False)
df_7


df_cf =  df.groupby(by='sector',as_index=False).agg({'funded_amount': np.sum}).sort_values(['funded_amount'],ascending=False)
df_cf.round(2)


# PLOT 2 TO BE EVALUATED

import plotly.graph_objects as go
from plotly.offline import iplot

trace1  = go.Scatter(
        mode='lines+markers',
        x = df_cf['sector'],
        y = df_cf['funded_amount'],
        name="total loan per sector",
        marker_color='chocolate'
    )

trace2 = go.Bar(
        x = df_s['sector'],
        y = df_s['applications'],
        name="total applications per sector",
        yaxis='y2',
        marker_color ='darkcyan',
        marker_line_width=1.5,
        marker_line_color='rgb(8,48,107)',
        opacity=0.5
    )

data = [trace1, trace2]

layout = go.Layout(
    title_text='Fig 4: Applications per sector and total investment 2016',
    template= "simple_white",
    yaxis=dict(
        range = [900000, 140000000],
        side = 'right'
        
    ),
    yaxis2=dict(
        overlaying='y',
        anchor='y3',
    )
)
fig = go.Figure(data=data, layout=layout)
iplot(fig, filename='multiple-axes-double')


df_mx =  df.groupby(by='sector',as_index=False).agg({'funded_amount': np.max}).sort_values(['funded_amount'],ascending=False)
df_mx.round(2)


#Is there any peculiarity with projects that cost 50,000 USD dollars and above?
df.loc[(df.funded_amount >= 50000) ,:]


#New column "inv_per_lender"

df["inv_per_lender"]=round((df['funded_amount']/df['lender_count']),2)

df["inv_per_lender"].fillna(0, inplace = True)
df.head(5)


df_is = df.groupby(by='sector',as_index=False).agg(applications_2016=('country', 'size'), funded_2016=('funded_amount', 'sum'), inv_per_lender=('inv_per_lender','mean') )                                                  
df_is


# PLOT 3 TO BE EVALUATED
import plotly.express as px
fig = px.scatter(data_frame=df_is, 
                 x="applications_2016",
                 y="funded_2016", 
                 color='sector',
                 size= "inv_per_lender",
                 size_max= 30,
                 title= " Fig 5.0 :Propensity to invest per sector KIVA 2016",
                 template="plotly_white",
                 labels={"funded_2016":"total invested in 2016", 'applications_2016':"total projects in 2016"}
                 
                 )
fig.show()


#Question One

# What is the repayment structure in these sectors (Clothing,food,and retail)? This would help further confirm our analysis
#We want to know why the propensity to invest in higher in this sectors.

df_rs = df.groupby(by=['repayment_interval',"sector"],as_index=False).agg(applications_2016=('sector', 'count'))                                                 
df_rs.head(60)


#For lack of time the following calculations were done on paper

#Bullet Form repayment

#agriculture 23%
#clothing 3.1%
#Retail 3.9%
#Food 3.3%


#Agriculture

df_ag = df.groupby('term_cat')['sector'].apply(lambda x: (x=='Agriculture').sum()).reset_index(name='count').sort_values(['count'],ascending=False)
df_ag


#For lack of time ,the following calculations were done on paper

# Short term loan 90%
#Medium term loan 9.6%
#long term 0.15%


#Clothing

df_ag = df.groupby('term_cat')['sector'].apply(lambda x: (x=='Clothing').sum()).reset_index(name='count').sort_values(['count'],ascending=False)
df_ag

#For lack of time ,the following calculations were done on paper

# Short term loan 94%
#Medium term loan 5.4%
#long term 0.06%
#extra long term 0


#Retail

df_ag = df.groupby('term_cat')['sector'].apply(lambda x: (x=='Retail').sum()).reset_index(name='count').sort_values(['count'],ascending=False)
df_ag

#For lack of time ,the following calculations were done on paper

# Short term loan 96%
#Medium term loan 3.2%
#long term 0.005%
#extra long term 0


#Clothing 

df_ac = df_1.groupby('activity')['sector'].apply(lambda x: (x=='Clothing').sum()).reset_index(name='count').sort_values(['count'],ascending=False)
df_ac.head(5)

fig = px.pie(df_ac.head(5), 
             values='count', 
             names='activity',
             title= "Fig 6.1: Activity distributed across the Clothing Sector"
            )


fig.update_traces(textposition='inside')
fig.update_layout(
    uniformtext_minsize=10, uniformtext_mode='hide')
    
    
fig.show()


#Retail 

df_ar = df.groupby('activity')['sector'].apply(lambda x: (x=='Retail').sum()).reset_index(name='count').sort_values(['count'],ascending=False)
df_ar

fig = px.pie(df_ar.head(34), 
             values='count', 
             names='activity',
             title= "fig 6.2 :Activity distributed across the Retail Sector"
            )


fig.update_traces(textposition='inside')
fig.update_layout(
    uniformtext_minsize=10, uniformtext_mode='hide')
    
    
fig.show()


#Food 

df_af = df.groupby('activity')['sector'].apply(lambda x: (x=='Food').sum()).reset_index(name='count').sort_values(['count'],ascending=False)
df_af.head(20)

fig = px.pie(df_af.head(20), 
             values='count', 
             names='activity',
             title= "fig 6.3: Activity distributed across the Food Sector"
            )


fig.update_traces(textposition='inside')
fig.update_layout(
 uniformtext_minsize=10, uniformtext_mode='hide')
    
    
fig.show()


df_csi = df.groupby(by=['country','sector'],as_index=False).agg(applications_2016=('sector', 'count'), funded_2016=('funded_amount', 'sum'), inv_per_lender=('inv_per_lender','mean') )                                                  
df_csi


import plotly.express as px
fig = px.scatter(data_frame=df_csi.loc[df_csi.country=="Kenya",:], 
                 x="applications_2016",
                 y="funded_2016", 
                 color='sector',
                 size= "inv_per_lender",
                 size_max= 30,
                 title= "Fig 5.1 : Propensity to invest per sector in Kenya 2016",
                 template="plotly_white",
                 labels={"funded_2016":"total invested in 2016", 'applications_2016':"total projects in 2016"}
                 
                 )
fig.show()


from jupyter_dash import JupyterDash
from dash import dcc


from dash import html


from dash.dependencies import Input, Output


# Dashboard Creation


external_stylesheet = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

my_kiva_app = JupyterDash(__name__, external_stylesheets=external_stylesheet)

my_kiva_app.layout = html.Div([
                 
                               html.H1("Kiva Country,Sector and Investor Analysis 2016",
                                      style={"text-align":"center"}
                                      ),
                       
                                html.Div([
                                      
                                      html.Label("Choose country:",
                                                style={'font-weight': 'bold'}),
                                     
    
                                    html.Br(), 
    
    
    
                                dcc.Dropdown(id="dd_kiva_plot",
                                            options=df_csi.country.unique(),
                                            style={ 'width': '250px'},
                                            
                                            )
                                             ]),
    
    
                               dcc.Graph(id="g_kiva_scatter",
                                        figure={},
                                        className="ten columns"
                                        ),
    
                              dcc.Graph(id="g_kiva_bar",
                                        figure={},
                                        className="ten columns"
                                        )
                            

                            ])

@my_kiva_app.callback( 
                       Output("g_kiva_scatter","figure"),
                       Output("g_kiva_bar","figure"),
    
                       
                      Input("dd_kiva_plot","value")
    
    
                      )

def update_kiva_figure(selected_country):
    
    
    df_sel= df_csi.loc[df_csi["country"]==selected_country,:]
    df_sel2=df_csi.loc[df_csi["country"]==selected_country,:]
    
    
    
    g_kiva_scatter=px.scatter(data_frame=df_sel,
                     x="applications_2016",
                     y="funded_2016",
                     color='sector',
                     size= "inv_per_lender",
                     size_max= 30,         
                     title="Investor behavior towards project vis a vis sector and country",
                     template="plotly_dark",
                     labels={"funded_2016":"total invested in 2016", 'applications_2016':"total projects in 2016"}
                      
                     )
    
    g_kiva_bar=px.bar(data_frame=df_sel,
                     x="sector",
                     y="applications_2016",
                     color='sector',
                     title="Applications per country",
                     template= "plotly_dark",
                     labels={"applications_2016":"Total projects 2016"}
                      
                     )
    
    
    
    return g_kiva_scatter,g_kiva_bar



#if __name__=="__main__":
 #   my_kiva_app.run_server(mode="external", port=8091)
    
    
if __name__=="__main__":

    my_kiva_app.run_server(mode='inline', port=8103)


#Business Figures

#Number of Contributions

df_1.lender_count.sum()

13655103


#Number of Applications
len(df_1.index)

646833


from plotly.offline import init_notebook_mode, iplot
init_notebook_mode(connected=True)

	Unnamed: 0	funded_amount	loan_amount	activity	sector	use	country_code	country	region	currency	term_in_months	lender_count	borrower_genders	repayment_interval
0	0	300.0	300.0	Fruits & Vegetables	Food	To buy seasonal, fresh fruits to sell.	PK	Pakistan	Lahore	PKR	12.0	12	female	irregular
1	1	575.0	575.0	Rickshaw	Transportation	to repair and maintain the auto rickshaw used ...	PK	Pakistan	Lahore	PKR	11.0	14	female, female	irregular
2	2	150.0	150.0	Transportation	Transportation	To repair their old cycle-van and buy another ...	IN	India	Maynaguri	INR	43.0	6	female	bullet
3	3	200.0	200.0	Embroidery	Arts	to purchase an embroidery machine and a variet...	PK	Pakistan	Lahore	PKR	11.0	8	female	irregular
4	4	400.0	400.0	Milk Sales	Food	to purchase one buffalo.	PK	Pakistan	Abdul Hakeem	PKR	14.0	16	female	monthly
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
671200	671200	0.0	25.0	Livestock	Agriculture	[True, u'para compara: cemento, arenya y ladri...	PY	Paraguay	Concepción	USD	13.0	0	female	monthly
671201	671201	25.0	25.0	Livestock	Agriculture	[True, u'to start a turducken farm.'] - this l...	KE	Kenya	NaN	KES	13.0	1	female	monthly
671202	671202	0.0	25.0	Games	Entertainment	NaN	KE	Kenya	NaN	KES	13.0	0	NaN	monthly
671203	671203	0.0	25.0	Livestock	Agriculture	[True, u'to start a turducken farm.'] - this l...	KE	Kenya	NaN	KES	13.0	0	female	monthly
671204	671204	0.0	25.0	Livestock	Agriculture	[True, u'to start a turducken farm.'] - this l...	KE	Kenya	NaN	KES	13.0	0	female	monthly

	funded_amount	loan_amount	activity	sector	use	country_code	country	region	currency	term_in_months	lender_count	borrower_genders	repayment_interval
498	100.0	100.0	Home Energy	Personal Use	to buy a solar-powered lamp.	SV	El Salvador	NaN	USD	14.0	4	male	monthly
606	100.0	100.0	Home Energy	Personal Use	to buy a solar-powered lamp.	SV	El Salvador	NaN	USD	14.0	4	male	monthly
808	450.0	450.0	Higher education costs	Education	to pay for one semester's registration fees.	CO	Colombia	Bogotà	COP	7.0	15	female	monthly
1703	500.0	500.0	Higher education costs	Education	To buy a laptop for educational purposes.	SO	Somalia	Hargeisa	USD	8.0	19	male	monthly
2317	250.0	250.0	Poultry	Agriculture	to purchase poultry.	KE	Kenya	Ndaragwa	KES	16.0	10	female	monthly
...	...	...	...	...	...	...	...	...	...	...	...	...	...
671200	0.0	25.0	Livestock	Agriculture	[True, u'para compara: cemento, arenya y ladri...	PY	Paraguay	Concepción	USD	13.0	0	female	monthly
671201	25.0	25.0	Livestock	Agriculture	[True, u'to start a turducken farm.'] - this l...	KE	Kenya	NaN	KES	13.0	1	female	monthly
671202	0.0	25.0	Games	Entertainment	NaN	KE	Kenya	NaN	KES	13.0	0	NaN	monthly
671203	0.0	25.0	Livestock	Agriculture	[True, u'to start a turducken farm.'] - this l...	KE	Kenya	NaN	KES	13.0	0	female	monthly
671204	0.0	25.0	Livestock	Agriculture	[True, u'to start a turducken farm.'] - this l...	KE	Kenya	NaN	KES	13.0	0	female	monthly

	funded_amount	loan_amount	activity	sector	use	country_code	country	region	currency	term_in_months	lender_count	borrower_genders	repayment_interval
140	2975.0	2975.0	Food Production/Sales	Food	NaN	TZ	Tanzania	NaN	TZS	10.0	110	NaN	monthly
145	1200.0	1200.0	Personal Expenses	Personal Use	NaN	PE	Peru	NaN	PEN	20.0	44	NaN	monthly
170	4250.0	4250.0	Catering	Food	NaN	TZ	Tanzania	NaN	TZS	10.0	116	NaN	monthly
412	2350.0	2350.0	Beauty Salon	Services	NaN	TZ	Tanzania	NaN	TZS	10.0	75	NaN	monthly
414	725.0	725.0	Agriculture	Agriculture	NaN	SV	El Salvador	NaN	USD	20.0	19	NaN	monthly
...	...	...	...	...	...	...	...	...	...	...	...	...	...
659604	5625.0	10000.0	Weaving	Arts	NaN	BT	Bhutan	NaN	USD	14.0	210	NaN	irregular
660788	1975.0	1975.0	Home Energy	Personal Use	NaN	PS	Palestine	NaN	USD	27.0	39	NaN	monthly
661718	800.0	1600.0	Furniture Making	Manufacturing	NaN	HT	Haiti	NaN	HTG	13.0	27	NaN	irregular
671151	0.0	25.0	Livestock	Agriculture	NaN	KE	Kenya	NaN	KES	13.0	0	NaN	monthly
671174	0.0	25.0	Games	Entertainment	NaN	KE	Kenya	NaN	KES	13.0	0	NaN	monthly

	funded_amount	loan_amount	activity	sector	use	country_code	country	region	currency	term_in_months	lender_count	borrower_genders	repayment_interval
140	2975.0	2975.0	Food Production/Sales	Food	NaN	TZ	Tanzania	NaN	TZS	10.0	110	NaN	monthly
145	1200.0	1200.0	Personal Expenses	Personal Use	NaN	PE	Peru	NaN	PEN	20.0	44	NaN	monthly
170	4250.0	4250.0	Catering	Food	NaN	TZ	Tanzania	NaN	TZS	10.0	116	NaN	monthly
412	2350.0	2350.0	Beauty Salon	Services	NaN	TZ	Tanzania	NaN	TZS	10.0	75	NaN	monthly
414	725.0	725.0	Agriculture	Agriculture	NaN	SV	El Salvador	NaN	USD	20.0	19	NaN	monthly
...	...	...	...	...	...	...	...	...	...	...	...	...	...
659604	5625.0	10000.0	Weaving	Arts	NaN	BT	Bhutan	NaN	USD	14.0	210	NaN	irregular
660788	1975.0	1975.0	Home Energy	Personal Use	NaN	PS	Palestine	NaN	USD	27.0	39	NaN	monthly
661718	800.0	1600.0	Furniture Making	Manufacturing	NaN	HT	Haiti	NaN	HTG	13.0	27	NaN	irregular
671151	0.0	25.0	Livestock	Agriculture	NaN	KE	Kenya	NaN	KES	13.0	0	NaN	monthly
671174	0.0	25.0	Games	Entertainment	NaN	KE	Kenya	NaN	KES	13.0	0	NaN	monthly

	funded_amount	loan_amount	activity	sector	use	country_code	country	region	currency	term_in_months	lender_count	borrower_genders	repayment_interval
5	250.0	250.0	Services	Services	purchase leather for my business using ksh 20000.	KE	Kenya	NaN	KES	4.0	6	female	irregular
49	450.0	450.0	General Store	Retail	to stock his store.	SV	El Salvador	NaN	USD	14.0	18	male	monthly
54	225.0	225.0	Food Market	Food	to purchase various seasonal items to resell: ...	SN	Senegal	NaN	XOF	14.0	7	female	monthly
67	125.0	125.0	Energy	Services	purchase solar lanterns for resale.	KE	Kenya	NaN	KES	3.0	6	male	irregular
70	2000.0	2000.0	Retail	Retail	to install a display window and a sunshade for...	IQ	Iraq	NaN	USD	15.0	71	male	monthly
...	...	...	...	...	...	...	...	...	...	...	...	...	...
671166	25.0	25.0	Livestock	Agriculture	[True, u'to start a turducken farm.'] - this l...	KE	Kenya	NaN	KES	13.0	1	female	monthly
671167	0.0	25.0	Livestock	Agriculture	Kiva Coordinator fixed issue loan (no longer v...	KE	Kenya	NaN	KES	13.0	0	female, female	monthly
671168	0.0	25.0	Livestock	Agriculture	Pretend the issue with loan got addressed by K...	KE	Kenya	NaN	KES	13.0	0	female	monthly
671171	0.0	25.0	Games	Entertainment	Kiva Coordinator replaced loan use. Should see...	KE	Kenya	NaN	KES	13.0	0	female, female	monthly
671174	0.0	25.0	Games	Entertainment	NaN	KE	Kenya	NaN	KES	13.0	0	NaN	monthly

Abschlussprojekt - Data Analytics¶

1. Teil - Datenanalyse¶

CRISP DM: Business Understanding¶

CRISP DM: Data Understanding¶

CRISP DM: Data Preparation¶

Cleaning my Data¶

Creating Features¶

Optimization and Storage¶

Explorative Data Analysis¶

How many projects were funded ?¶

Which countries had the most applications?¶

What sectors have the most applications?¶

What sector had the highest inflow of cash?¶

Lender/Investor Behavior towards projects vis a vis sectors and Countries¶

FINAL RESULTS FROM OUR EDA¶

2. Teil - Dashboard¶

Conclusion¶

	funded_amount	loan_amount	activity	sector	use	country_code	country	region	currency	term_in_months	lender_count	borrower_genders	repayment_interval
202537	4150.0	4150.0	Wholesale	Wholesale	To purchase lighting products for sale to loca...	NaN	Namibia	EEnhana	NAD	6.0	162	female	bullet
202823	4150.0	4150.0	Wholesale	Wholesale	To purchase lighting products for sale to loca...	NaN	Namibia	Rundu	NAD	6.0	159	male	bullet
344929	3325.0	3325.0	Wholesale	Wholesale	To purchase lighting products for sale to loca...	NaN	Namibia	EEnhana	NAD	7.0	120	female	bullet
351177	3325.0	3325.0	Wholesale	Wholesale	To purchase lighting products for sale to loca...	NaN	Namibia	Rundu	NAD	7.0	126	male	bullet
420953	3325.0	3325.0	Wholesale	Wholesale	To purchase lighting products for sale to loca...	NaN	Namibia	EEnhana	NAD	7.0	118	female	bullet
421218	4000.0	4000.0	Wholesale	Wholesale	purchase solar lighting products for sale to l...	NaN	Namibia	Rundu	NAD	7.0	150	male	bullet
487207	5100.0	5100.0	Renewable Energy Products	Retail	to pay for stock of solar lights and cell phon...	NaN	Namibia	Katima Mulilo	NAD	7.0	183	male	bullet
487653	5000.0	5000.0	Wholesale	Wholesale	to maintain a stock of solar lights and cell p...	NaN	Namibia	Oshakati	NAD	7.0	183	female	bullet

	loan_status	count_cat
1	fully_funded	598796
0	avg_funding	17808
6	very_poor_funding	11878
5	poor_funding	9946
2	good_funding	5125
3	not_funded	3278
4	over_funded	2

	funded_amount	loan_amount	activity	sector	use	country_code	country	currency	term_in_months	lender_count	borrower_genders	repayment_interval	term_cat	term_label	rate_of_financing(%)	loan_status
4814	0.0	5000.0	Food Production/Sales	Food	grow and create new jobs in my company.	US	United States	USD	24.0	0	female	bullet	short_term_loan	(0.999, 25.0]	0.0	not_funded
6784	0.0	10000.0	Cosmetics Sales	Retail	start a new stream of income by offering retai...	US	United States	USD	36.0	0	male	bullet	medium_term_loan	(25.0, 61.0]	0.0	not_funded
10735	0.0	5000.0	Services	Services	purchase new equipment and market to more pote...	US	United States	USD	24.0	0	male	bullet	short_term_loan	(0.999, 25.0]	0.0	not_funded
12037	0.0	5000.0	Food Production/Sales	Food	purchase much needed packaging that is sustain...	US	United States	USD	24.0	0	male	bullet	short_term_loan	(0.999, 25.0]	0.0	not_funded
13542	0.0	1500.0	Crafts	Arts	increase my inventory of handmade candles, dri...	US	United States	USD	12.0	0	male	bullet	short_term_loan	(0.999, 25.0]	0.0	not_funded
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
671179	0.0	25.0	Livestock	Agriculture	[True, u'para compara: cemento, arenya y ladri...	PY	Paraguay	USD	13.0	0	female	monthly	short_term_loan	(0.999, 25.0]	0.0	not_funded
671181	0.0	25.0	Livestock	Agriculture	Reviewed loan use in english.	PK	Pakistan	PKR	13.0	0	female	monthly	short_term_loan	(0.999, 25.0]	0.0	not_funded
671182	0.0	125.0	Livestock	Agriculture	Pretend the flagged issue was addressed by KC.	MX	Mexico	MXN	13.0	0	female, female	monthly	short_term_loan	(0.999, 25.0]	0.0	not_funded
671184	0.0	875.0	Livestock	Agriculture	Translated loan use to english.	BO	Bolivia	BOB	13.0	0	female, female	monthly	short_term_loan	(0.999, 25.0]	0.0	not_funded
671188	0.0	250.0	Livestock	Agriculture	Reviewed loan use in english.	GH	Ghana	GHS	13.0	0	female	monthly	short_term_loan	(0.999, 25.0]	0.0	not_funded

	funded_amount	loan_amount	activity	sector	use	country_code	country	currency	term_in_months	lender_count	borrower_genders	repayment_interval	term_cat	term_label	rate_of_financing(%)	loan_status
657556	0.0	1000.0	Tourism	Services	Reviewed loan use in english.	KH	Cambodia	KHR	12.0	0	female, female	monthly	short_term_loan	(0.999, 25.0]	0.00	not_funded
657584	0.0	700.0	Tourism	Services	Reviewed loan use in english.	KH	Cambodia	KHR	9.0	0	female, female	monthly	short_term_loan	(0.999, 25.0]	0.00	not_funded
657717	0.0	1975.0	Tourism	Services	Reviewed loan use in english.	KH	Cambodia	KHR	25.0	0	female, female, female, female	monthly	short_term_loan	(0.999, 25.0]	0.00	not_funded
658107	0.0	925.0	Tourism	Services	Reviewed loan use in english.	KH	Cambodia	KHR	11.0	0	female, female	monthly	short_term_loan	(0.999, 25.0]	0.00	not_funded
658437	0.0	2075.0	Tourism	Services	Reviewed loan use in english.	KH	Cambodia	KHR	26.0	0	female, female, female	monthly	medium_term_loan	(25.0, 61.0]	0.00	not_funded
659111	0.0	1000.0	Tourism	Services	Reviewed loan use in english.	KH	Cambodia	KHR	20.0	0	female, female	monthly	short_term_loan	(0.999, 25.0]	0.00	not_funded
661362	0.0	625.0	Transportation	Transportation	Translated loan use to english.	PE	Peru	PEN	8.0	0	male	monthly	short_term_loan	(0.999, 25.0]	0.00	not_funded
661679	0.0	1550.0	Dental	Health	Translated loan use to english.	PE	Peru	PEN	8.0	0	male	monthly	short_term_loan	(0.999, 25.0]	0.00	not_funded
661941	0.0	925.0	Tourism	Services	Translated loan use to english.	PE	Peru	PEN	8.0	0	male	monthly	short_term_loan	(0.999, 25.0]	0.00	not_funded
661956	0.0	1550.0	Restaurant	Food	Translated loan use to english.	PE	Peru	PEN	8.0	0	female	monthly	short_term_loan	(0.999, 25.0]	0.00	not_funded
662377	0.0	6425.0	Sewing	Services	Translated loan use to english.	PE	Peru	PEN	6.0	0	male, female, male, male, male, male, female, ...	irregular	short_term_loan	(0.999, 25.0]	0.00	not_funded
662552	0.0	6575.0	Grocery Store	Food	Translated loan use to english.	PE	Peru	PEN	8.0	0	male, male, female, female, female, female, fe...	monthly	short_term_loan	(0.999, 25.0]	0.00	not_funded
662746	0.0	625.0	Cosmetics Sales	Retail	Translated loan use to english.	PE	Peru	PEN	8.0	0	female	monthly	short_term_loan	(0.999, 25.0]	0.00	not_funded
662793	0.0	625.0	Electronics Sales	Retail	Translated loan use to english.	PE	Peru	PEN	8.0	0	male	monthly	short_term_loan	(0.999, 25.0]	0.00	not_funded
662846	0.0	1225.0	Tourism	Services	Translated loan use to english.	PE	Peru	PEN	8.0	0	male	monthly	short_term_loan	(0.999, 25.0]	0.00	not_funded
662849	0.0	775.0	Clothing Sales	Clothing	Translated loan use to english.	PE	Peru	PEN	8.0	0	female	monthly	short_term_loan	(0.999, 25.0]	0.00	not_funded
663601	0.0	5525.0	Retail	Retail	Translated loan use to english.	PE	Peru	PEN	7.0	0	male, female, female, female, female, female, ...	monthly	short_term_loan	(0.999, 25.0]	0.00	not_funded
663824	0.0	1075.0	Home Appliances	Personal Use	Translated loan use to english.	PE	Peru	PEN	6.0	0	female	irregular	short_term_loan	(0.999, 25.0]	0.00	not_funded
663855	0.0	1700.0	Transportation	Transportation	Translated loan use to english.	PE	Peru	PEN	8.0	0	female, female, female, female, male	monthly	short_term_loan	(0.999, 25.0]	0.00	not_funded
664078	0.0	1250.0	Retail	Retail	Translated loan use to english.	PE	Peru	PEN	6.0	0	female	irregular	short_term_loan	(0.999, 25.0]	0.00	not_funded
670969	0.0	675.0	Retail	Retail	Translated loan use to english.	CO	Colombia	COP	26.0	0	female	bullet	medium_term_loan	(25.0, 61.0]	0.00	not_funded
671027	0.0	300.0	Cattle	Agriculture	Translated loan use to english.	SV	El Salvador	USD	15.0	0	male	bullet	short_term_loan	(0.999, 25.0]	0.00	not_funded
671092	0.0	1000.0	Cattle	Agriculture	Translated loan use to english.	EC	Ecuador	USD	38.0	0	female	monthly	medium_term_loan	(25.0, 61.0]	0.00	not_funded
671150	0.0	75.0	Livestock	Agriculture	Pretend the issue with spanish loan was addres...	MX	Mexico	MXN	13.0	0	female	monthly	short_term_loan	(0.999, 25.0]	0.00	not_funded
671152	25.0	25.0	Livestock	Agriculture	Reviewed loan use in english.	PK	Pakistan	PKR	13.0	1	female	monthly	short_term_loan	(0.999, 25.0]	100.00	fully_funded
671153	75.0	125.0	Livestock	Agriculture	Pretend the flagged issue was addressed by KC.	MX	Mexico	MXN	13.0	3	female, female	monthly	short_term_loan	(0.999, 25.0]	60.00	avg_funding
671154	0.0	725.0	Livestock	Agriculture	Translated loan use to english.	BO	Bolivia	BOB	13.0	0	female	monthly	short_term_loan	(0.999, 25.0]	0.00	not_funded
671155	50.0	875.0	Livestock	Agriculture	Translated loan use to english.	BO	Bolivia	BOB	13.0	2	female, female	monthly	short_term_loan	(0.999, 25.0]	5.71	very_poor_funding
671156	75.0	250.0	Livestock	Agriculture	Reviewed loan use in english.	GH	Ghana	GHS	13.0	3	female	monthly	short_term_loan	(0.999, 25.0]	30.00	very_poor_funding
671157	75.0	75.0	Livestock	Agriculture	Pretend the issue with spanish loan was addres...	MX	Mexico	MXN	13.0	3	female	monthly	short_term_loan	(0.999, 25.0]	100.00	fully_funded
671159	50.0	725.0	Livestock	Agriculture	Translated loan use to english.	BO	Bolivia	BOB	13.0	2	female	monthly	short_term_loan	(0.999, 25.0]	6.90	very_poor_funding
671167	0.0	25.0	Livestock	Agriculture	Kiva Coordinator fixed issue loan (no longer v...	KE	Kenya	KES	13.0	0	female, female	monthly	short_term_loan	(0.999, 25.0]	0.00	not_funded
671168	0.0	25.0	Livestock	Agriculture	Pretend the issue with loan got addressed by K...	KE	Kenya	KES	13.0	0	female	monthly	short_term_loan	(0.999, 25.0]	0.00	not_funded
671171	0.0	25.0	Games	Entertainment	Kiva Coordinator replaced loan use. Should see...	KE	Kenya	KES	13.0	0	female, female	monthly	short_term_loan	(0.999, 25.0]	0.00	not_funded
671181	0.0	25.0	Livestock	Agriculture	Reviewed loan use in english.	PK	Pakistan	PKR	13.0	0	female	monthly	short_term_loan	(0.999, 25.0]	0.00	not_funded
671182	0.0	125.0	Livestock	Agriculture	Pretend the flagged issue was addressed by KC.	MX	Mexico	MXN	13.0	0	female, female	monthly	short_term_loan	(0.999, 25.0]	0.00	not_funded
671184	0.0	875.0	Livestock	Agriculture	Translated loan use to english.	BO	Bolivia	BOB	13.0	0	female, female	monthly	short_term_loan	(0.999, 25.0]	0.00	not_funded
671188	0.0	250.0	Livestock	Agriculture	Reviewed loan use in english.	GH	Ghana	GHS	13.0	0	female	monthly	short_term_loan	(0.999, 25.0]	0.00	not_funded

	funded_amount	loan_amount	activity	sector	use	country_code	country	currency	term_in_months	lender_count	borrower_genders	repayment_interval	term_cat	term_label	rate_of_financing(%)	loan_status
36075	0.0	600.0	Personal Medical Expenses	Health	not_provided	BO	Bolivia	USD	20.0	0	female	monthly	short_term_loan	(0.999, 25.0]	0.0	not_funded
40489	0.0	1050.0	Cloth & Dressmaking Supplies	Retail	not_provided	KE	Kenya	KES	14.0	0	female	monthly	short_term_loan	(0.999, 25.0]	0.0	not_funded
53392	0.0	1025.0	Retail	Retail	not_provided	NI	Nicaragua	USD	17.0	0	female	monthly	short_term_loan	(0.999, 25.0]	0.0	not_funded
59399	0.0	325.0	Agriculture	Agriculture	not_provided	SV	El Salvador	USD	13.0	0	female	bullet	short_term_loan	(0.999, 25.0]	0.0	not_funded
61296	0.0	500.0	Farming	Agriculture	not_provided	SV	El Salvador	USD	14.0	0	female	irregular	short_term_loan	(0.999, 25.0]	0.0	not_funded
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
643472	0.0	425.0	Farming	Agriculture	not_provided	UG	Uganda	UGX	12.0	0	female	monthly	short_term_loan	(0.999, 25.0]	0.0	not_funded
646452	0.0	3000.0	Clothing	Clothing	not_provided	US	United States	USD	24.0	0	female	bullet	short_term_loan	(0.999, 25.0]	0.0	not_funded
647114	0.0	5000.0	Education provider	Education	not_provided	US	United States	USD	18.0	0	female	bullet	short_term_loan	(0.999, 25.0]	0.0	not_funded
671151	0.0	25.0	Livestock	Agriculture	not_provided	KE	Kenya	KES	13.0	0	female	monthly	short_term_loan	(0.999, 25.0]	0.0	not_funded
671174	0.0	25.0	Games	Entertainment	not_provided	KE	Kenya	KES	13.0	0	female	monthly	short_term_loan	(0.999, 25.0]	0.0	not_funded

	sector	count
0	Agriculture	160999
6	Food	127300
11	Retail	112921
12	Services	40966
2	Clothing	29572

	country	number_of_applications
59	Philippines	154323
34	Kenya	73554
22	El Salvador	39845
11	Cambodia	27067
54	Pakistan	26475

	sector	count
11	Retail	52510
6	Food	41864
0	Agriculture	35158
8	Housing	4697
13	Transportation	4547

	sector	count
0	Agriculture	32883
6	Food	13763
11	Retail	10094
12	Services	5408
2	Clothing	4667

	sector	count
0	Agriculture	14008
6	Food	8307
8	Housing	5965
11	Retail	5666
2	Clothing	1905

	sector	applications_2016	funded_2016	inv_per_lender
0	Agriculture	174624	132669060.0	47.569026
1	Arts	11938	11778255.0	60.762197
2	Clothing	32554	34750195.0	78.995450
3	Construction	6263	6320365.0	56.376962
4	Education	30128	29701870.0	39.877026
5	Entertainment	829	1023095.0	48.978299
6	Food	135359	114770835.0	77.002489
7	Health	9190	9159825.0	51.929370
8	Housing	30155	20191325.0	39.647853
9	Manufacturing	6204	5355900.0	52.163453
10	Personal Use	25533	12692225.0	42.697106
11	Retail	123186	90523555.0	77.134103
12	Services	44844	42879735.0	62.618802
13	Transportation	15392	9805775.0	53.756249
14	Wholesale	634	918900.0	46.627666

	term_cat	count
0	short_term_loan	157336
1	medium_term_loan	16933
2	long_term_loan	277
3	extra_long_term_loan	78

	term_cat	count
0	short_term_loan	30772
1	medium_term_loan	1760
2	long_term_loan	22
3	extra_long_term_loan	0

	term_cat	count
0	short_term_loan	119212
1	medium_term_loan	3967
2	long_term_loan	7
3	extra_long_term_loan	0

	sector	funded_amount
0	Agriculture	100000.0
2	Clothing	50000.0
3	Construction	50000.0
4	Education	50000.0
7	Health	50000.0
9	Manufacturing	50000.0
11	Retail	50000.0
12	Services	50000.0
14	Wholesale	50000.0
1	Arts	40000.0
6	Food	35000.0
13	Transportation	35000.0
8	Housing	24450.0
10	Personal Use	13425.0
5	Entertainment	10000.0

	funded_amount	loan_amount	activity	sector	use	country_code	country	currency	term_in_months	lender_count	borrower_genders	repayment_interval	term_cat	term_label	rate_of_financing(%)	loan_status
34196	50000.0	50000.0	Renewable Energy Products	Retail	to buy and sell Barefoot Power's Solar Lightin...	PE	Peru	USD	14.0	1446	male	irregular	short_term_loan	(0.999, 25.0]	100.0	fully_funded
43182	50000.0	50000.0	Renewable Energy Products	Retail	To buy and sell Barefoot Power's solar lightin...	KE	Kenya	USD	16.0	1491	male	bullet	short_term_loan	(0.999, 25.0]	100.0	fully_funded
53634	50000.0	50000.0	Renewable Energy Products	Retail	To buy and sell Barefoot Power solar lighting.	UG	Uganda	USD	14.0	1581	female	bullet	short_term_loan	(0.999, 25.0]	100.0	fully_funded
70499	100000.0	100000.0	Agriculture	Agriculture	create more than 300 jobs for women and farmer...	HT	Haiti	USD	75.0	2986	female	irregular	long_term_loan	(61.0, 121.0]	100.0	fully_funded
126839	50000.0	50000.0	Agriculture	Agriculture	to buy and plant resin producing pine trees. T...	MX	Mexico	USD	144.0	586	male, male, male, male, male, male, male, female	irregular	extra_long_term_loan	(121.0, 158.158]	100.0	fully_funded
163727	50000.0	50000.0	Agriculture	Agriculture	to fund its growing loan book and further deve...	KE	Kenya	USD	38.0	1343	female	bullet	medium_term_loan	(25.0, 61.0]	100.0	fully_funded
210975	50000.0	50000.0	Agriculture	Agriculture	To work with 17 farming cooperatives to proces...	RW	Rwanda	USD	8.0	1302	male	bullet	short_term_loan	(0.999, 25.0]	100.0	fully_funded
223120	50000.0	50000.0	Higher education costs	Education	to provide loans and career services for the l...	MX	Mexico	USD	14.0	960	male	irregular	short_term_loan	(0.999, 25.0]	100.0	fully_funded
408295	50000.0	50000.0	Clothing	Clothing	to set up a garment social business that will ...	AL	Albania	USD	97.0	1626	male	monthly	long_term_loan	(61.0, 121.0]	100.0	fully_funded
408465	50000.0	50000.0	Construction	Construction	not_provided	PE	Peru	USD	73.0	1310	female	irregular	long_term_loan	(61.0, 121.0]	100.0	fully_funded
447374	50000.0	50000.0	Agriculture	Agriculture	to increase smallholder farmers’ incomes by bu...	UG	Uganda	USD	13.0	1555	male	monthly	short_term_loan	(0.999, 25.0]	100.0	fully_funded
490191	50000.0	50000.0	Health	Health	To purchase raw materials in order to produce ...	GH	Ghana	USD	14.0	1569	male	irregular	short_term_loan	(0.999, 25.0]	100.0	fully_funded
492809	50000.0	50000.0	Agriculture	Agriculture	to expand weather, farming information and fin...	GH	Ghana	USD	14.0	1481	male	irregular	short_term_loan	(0.999, 25.0]	100.0	fully_funded
494470	50000.0	50000.0	Agriculture	Agriculture	To pay smallholder coffee farmers in rural Ken...	KE	Kenya	USD	15.0	1441	female	irregular	short_term_loan	(0.999, 25.0]	100.0	fully_funded
496715	50000.0	50000.0	Agriculture	Agriculture	to fund the harvest of seeds of 6,000 smallhol...	MG	Madagascar	USD	22.0	1606	male	irregular	short_term_loan	(0.999, 25.0]	100.0	fully_funded
509048	50000.0	50000.0	Poultry	Agriculture	to purchase chicken feed & a delivery vehicle ...	TZ	Tanzania	USD	14.0	1765	female	irregular	short_term_loan	(0.999, 25.0]	100.0	fully_funded
523634	50000.0	50000.0	Health	Health	to mitigate CO2 & household air pollution, whi...	MW	Malawi	USD	24.0	1465	male	irregular	short_term_loan	(0.999, 25.0]	100.0	fully_funded
523659	50000.0	50000.0	Electronics Sales	Retail	to train & equip 200 rural merchants in Mozamb...	MZ	Mozambique	USD	14.0	1410	female	irregular	short_term_loan	(0.999, 25.0]	100.0	fully_funded
526100	50000.0	50000.0	Renewable Energy Products	Retail	to distribute 200+ innovative & affordable pay...	ZM	Zambia	USD	14.0	1550	male	irregular	short_term_loan	(0.999, 25.0]	100.0	fully_funded
538248	50000.0	50000.0	Agriculture	Agriculture	to enable 5,000 additional small-holder farmer...	KE	Kenya	USD	14.0	1589	male	monthly	short_term_loan	(0.999, 25.0]	100.0	fully_funded
541006	50000.0	50000.0	Goods Distribution	Wholesale	to bolster logistics of affordable water distr...	HT	Haiti	USD	14.0	1349	male	irregular	short_term_loan	(0.999, 25.0]	100.0	fully_funded
544548	50000.0	50000.0	Health	Health	to provide community trauma services in South ...	SS	South Sudan	USD	8.0	1609	female	bullet	short_term_loan	(0.999, 25.0]	100.0	fully_funded
548513	50000.0	50000.0	Renewable Energy Products	Retail	to distribute solar home systems throughout ru...	ZW	Zimbabwe	USD	13.0	720	male	monthly	short_term_loan	(0.999, 25.0]	100.0	fully_funded
563074	50000.0	50000.0	Renewable Energy Products	Retail	to provide life-changing clean cookstoves and ...	KE	Kenya	USD	14.0	1402	female	irregular	short_term_loan	(0.999, 25.0]	100.0	fully_funded
565733	50000.0	50000.0	Agriculture	Agriculture	to pay 600 farming families 100% above market ...	EC	Ecuador	USD	14.0	1689	female	irregular	short_term_loan	(0.999, 25.0]	100.0	fully_funded
583307	50000.0	50000.0	Agriculture	Agriculture	to support 800+ farmers by improving their pro...	GT	Guatemala	USD	20.0	1671	male	monthly	short_term_loan	(0.999, 25.0]	100.0	fully_funded
586970	50000.0	50000.0	Renewable Energy Products	Retail	to generate income to over 600 fishermen in Ta...	TZ	Tanzania	USD	14.0	782	female	irregular	short_term_loan	(0.999, 25.0]	100.0	fully_funded
604502	50000.0	50000.0	Agriculture	Agriculture	to add value and jobs to the local economy by ...	BJ	Benin	USD	12.0	1519	male	irregular	short_term_loan	(0.999, 25.0]	100.0	fully_funded
614869	50000.0	50000.0	Furniture Making	Manufacturing	create jobs through environmentally-friendly m...	KE	Kenya	USD	20.0	1094	male	irregular	short_term_loan	(0.999, 25.0]	100.0	fully_funded
614922	50000.0	50000.0	Water Distribution	Services	to set up 13 new clean water businesses in nor...	GH	Ghana	USD	14.0	1688	female	irregular	short_term_loan	(0.999, 25.0]	100.0	fully_funded
618264	50000.0	50000.0	Farming	Agriculture	to provide income opportunities in remote Indo...	ID	Indonesia	USD	14.0	1564	male	irregular	short_term_loan	(0.999, 25.0]	100.0	fully_funded
621860	50000.0	50000.0	Renewable Energy Products	Retail	to distribute 400 pay-as-you-go solar home sys...	KE	Kenya	USD	14.0	1170	male	irregular	short_term_loan	(0.999, 25.0]	100.0	fully_funded
631904	50000.0	50000.0	Agriculture	Agriculture	double cashew nut export output and hire about...	CI	Cote D'Ivoire	USD	10.0	1706	female	irregular	short_term_loan	(0.999, 25.0]	100.0	fully_funded

	repayment_interval	sector	applications_2016
0	bullet	Agriculture	41050
1	bullet	Arts	1454
2	bullet	Clothing	1037
3	bullet	Construction	293
4	bullet	Education	2156
5	bullet	Entertainment	155
6	bullet	Food	4583
7	bullet	Health	138
8	bullet	Housing	875
9	bullet	Manufacturing	451
10	bullet	Personal Use	3134
11	bullet	Retail	4896
12	bullet	Services	5052
13	bullet	Transportation	406
14	bullet	Wholesale	56
15	irregular	Agriculture	45716
16	irregular	Arts	4605
17	irregular	Clothing	13447
18	irregular	Construction	1547
19	irregular	Education	4288
20	irregular	Entertainment	226
21	irregular	Food	74882
22	irregular	Health	1654
23	irregular	Housing	6285
24	irregular	Manufacturing	2436
25	irregular	Personal Use	3522
26	irregular	Retail	72486
27	irregular	Services	12433
28	irregular	Transportation	6360
29	irregular	Wholesale	164
30	monthly	Agriculture	87819
31	monthly	Arts	5864
32	monthly	Clothing	17936
33	monthly	Construction	4412
34	monthly	Education	23684
35	monthly	Entertainment	448
36	monthly	Food	55797
37	monthly	Health	7385
38	monthly	Housing	22995
39	monthly	Manufacturing	3317
40	monthly	Personal Use	18877
41	monthly	Retail	45680
42	monthly	Services	27206
43	monthly	Transportation	8614
44	monthly	Wholesale	414
45	weekly	Agriculture	39
46	weekly	Arts	15
47	weekly	Clothing	134
48	weekly	Construction	11
49	weekly	Food	97
50	weekly	Health	13
51	weekly	Retail	124
52	weekly	Services	153
53	weekly	Transportation	12

	country	sector	applications_2016	funded_2016	inv_per_lender
0	Afghanistan	Arts	2	14000.0	27.690000
1	Albania	Agriculture	719	976925.0	31.333060
2	Albania	Arts	5	8375.0	29.556000
3	Albania	Clothing	87	153925.0	31.759770
4	Albania	Construction	30	35325.0	31.784333
...	...	...	...	...	...
985	Zimbabwe	Personal Use	1	1400.0	53.850000
986	Zimbabwe	Retail	765	764500.0	39.301399
987	Zimbabwe	Services	230	209875.0	39.358391
988	Zimbabwe	Transportation	9	11550.0	29.873333
989	Zimbabwe	Wholesale	16	22300.0	33.176875